Machine learning for dynamically updating a user interface

ABSTRACT

Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts to determine predicted recovery rates/scores for claims, determine priority scores for the claims, and prioritizing the claims based on the same, and updating a user interface based at least in part on the prioritization of the same.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.16/021,289 filed Jun. 28, 2018, the contents of which are herebyincorporated by reference in its entirety.

TECHNOLOGICAL FIELD

Embodiments of the present invention generally relate tomachine-learning based methodologies for automatic determinations ofrisk scores indicative of the likelihood of an overpayment beingrecoverable, automatic determinations of prioritization scores, anddynamic updates for a user interface regarding the same.

BACKGROUND

As will be recognized, a common problem in claim processing is theoverpayment of claims. The causes of overpayments can stem from avariety of reasons, such as claim processing errors, duplicate payments,system limitations, contract errors, and/or the like. Existingmethodologies exist to present users (e.g., recovery agents) with theability to recover all or some portion of the overpayments. However,such methodologies have a variety of shortcomings—chief among them beingthat overpaid claims are presented to users for recovery based on therecoverable amounts of the claims (e.g., they are typically sorted inorder by dollar amount and presented via an interface). However, suchexisting methodologies do not necessarily present the overpaid claims inan order that yields the best recovery results.

Accordingly, there is a latent need for a rigorous methodology that canpredict overpaid claims with the highest likelihood of recovery,determine a priority accordingly, and dynamically present the same via auser interface. This ability would provide actionable insight to users(e.g., recovery agents) and enable them to target the overpaid claims ina much more effective manner. Through applied effort, ingenuity, andinnovation, the inventors have developed systems and methods thatproduce such predictions, scores, and dynamic interface updates. Someexamples of these solutions are described in detail herein.

BRIEF SUMMARY

In general, embodiments of the present invention provide methods,apparatus, systems, computing devices, computing entities, and/or thelike.

In accordance with one aspect, a method is provided. In one embodiment,the method comprises receiving, by one or more processors, a first claimin real-time for electronic storage in an open claim inventory;determining, by the one or more processors, a predicted recovery scorefor the first claim using one or more machine learning models, thepredicted recovery score indicative of the likelihood that thecorresponding claim is recoverable; determining, by the one or moreprocessors, a priority score for the first claim, wherein the priorityscore for the first claim is based at least in part on the predictedrecovery score for the first claim and a first recoverable amount forthe first claim; inserting, by the one or more processors, the firstclaim to a queue data structure, wherein the data structure (a) isassociated with a first plurality of claims, and (b) comprises anindication of the priority score for first claim and respective priorityscores for the first plurality of claims; prioritizing, by the one ormore processors, the first claim and the first plurality of claims intoa second plurality of claims; and dynamically providing, by the one ormore processors, an update to the user interface to display at least aportion of the second plurality of claims.

In accordance with another aspect, a computer program product isprovided. The computer program product may comprise at least onecomputer-readable storage medium having computer-readable program codeportions stored therein, the computer-readable program code portionscomprising executable portions configured to receive a first claim inreal-time for electronic storage in an open claim inventory; determine apredicted recovery score for the first claim using one or more machinelearning models, the predicted recovery score indicative of thelikelihood that the corresponding claim is recoverable; determine apriority score for the first claim, wherein the priority score for thefirst claim is based at least in part on the predicted recovery scorefor the first claim and a first recoverable amount for the first claim;insert the first claim to a queue data structure, wherein the datastructure (a) is associated with a first plurality of claims, and (b)comprises an indication of the priority score for first claim andrespective priority scores for the first plurality of claims; prioritizethe first claim and the first plurality of claims into a secondplurality of claims; and dynamically provide an update to the userinterface to display at least a portion of the second plurality ofclaims.

In accordance with yet another aspect, a computing system comprising atleast one processor and at least one memory including computer programcode is provided. In one embodiment, the at least one memory and thecomputer program code may be configured to, with the processor, causethe apparatus to receive a first claim in real-time for electronicstorage in an open claim inventory; determine a predicted recovery scorefor the first claim using one or more machine learning models, thepredicted recovery score indicative of the likelihood that thecorresponding claim is recoverable; determine a priority score for thefirst claim, wherein the priority score for the first claim is based atleast in part on the predicted recovery score for the first claim and afirst recoverable amount for the first claim; insert the first claim toa queue data structure, wherein the data structure (a) is associatedwith a first plurality of claims, and (b) comprises an indication of thepriority score for first claim and respective priority scores for thefirst plurality of claims; prioritize the first claim and the firstplurality of claims into a second plurality of claims; and dynamicallyprovide an update to the user interface to display at least a portion ofthe second plurality of claims.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

Having thus described the invention in general terms, reference will nowbe made to the accompanying drawings, which are not necessarily drawn toscale, and wherein:

FIG. 1 is a diagram of a recovery prediction system that can be used inconjunction with various embodiments of the present invention;

FIG. 2A is a schematic of an analytic computing entity in accordancewith certain embodiments of the present invention;

FIG. 2B is a schematic representation of a memory media storing aplurality of repositories, databases, and/or relational tables;

FIG. 3 is a schematic of a user computing entity in accordance withcertain embodiments of the present invention;

FIGS. 4A, 4B, 4C, and 4D are examples of claim features for use inmachine learning in accordance with certain embodiments of the presentinvention;

FIG. 5 is a flowchart for exemplary operations, steps, and processes inaccordance with certain embodiments of the present invention; and

FIG. 6 provides and an interactive user interface dynamically updatedbased at least in part on a machine learning model in accordance withembodiments of the present invention.

DETAILED DESCRIPTION OF SOME EXAMPLE EMBODIMENTS

Various embodiments of the present invention now will be described morefully hereinafter with reference to the accompanying drawings, in whichsome, but not all embodiments of the inventions are shown. Indeed, theseinventions may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. The term “or” (also designated as “/”) is usedherein in both the alternative and conjunctive sense, unless otherwiseindicated. The terms “illustrative” and “exemplary” are used to beexamples with no indication of quality level. Like numbers refer to likeelements throughout.

I. Computer Program Products, Methods, and Computing Entities

Embodiments of the present invention may be implemented in various ways,including as computer program products that comprise articles ofmanufacture. Such computer program products may include one or moresoftware components including, for example, software objects, methods,data structures, and/or the like. A software component may be coded inany of a variety of programming languages. An illustrative programminglanguage may be a lower-level programming language such as an assemblylanguage associated with a particular hardware architecture and/oroperating system platform. A software component comprising assemblylanguage instructions may require conversion into executable machinecode by an assembler prior to execution by the hardware architectureand/or platform. Another example programming language may be ahigher-level programming language that may be portable across multiplearchitectures. A software component comprising higher-level programminglanguage instructions may require conversion to an intermediaterepresentation by an interpreter or a compiler prior to execution.

Other examples of programming languages include, but are not limited to,a macro language, a shell or command language, a job control language, ascript language, a database query or search language, and/or a reportwriting language. In one or more example embodiments, a softwarecomponent comprising instructions in one of the foregoing examples ofprogramming languages may be executed directly by an operating system orother software component without having to be first transformed intoanother form. A software component may be stored as a file or other datastorage construct. Software components of a similar type or functionallyrelated may be stored together such as, for example, in a particulardirectory, folder, or library. Software components may be static (e.g.,pre-established or fixed) or dynamic (e.g., created or modified at thetime of execution).

A computer program product may include a non-transitorycomputer-readable storage medium storing applications, programs, programmodules, scripts, source code, program code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like (also referred to herein as executable instructions,instructions for execution, computer program products, program code,and/or similar terms used herein interchangeably). Such non-transitorycomputer-readable storage media include all computer-readable media(including volatile and non-volatile media).

In one embodiment, a non-volatile computer-readable storage medium mayinclude a floppy disk, flexible disk, hard disk, solid-state storage(SSS) (e.g., a solid state drive (SSD), solid state card (SSC), solidstate module (SSM), enterprise flash drive, magnetic tape, or any othernon-transitory magnetic medium, and/or the like. A non-volatilecomputer-readable storage medium may also include a punch card, papertape, optical mark sheet (or any other physical medium with patterns ofholes or other optically recognizable indicia), compact disc read onlymemory (CD-ROM), compact disc-rewritable (CD-RW), digital versatile disc(DVD), Blu-ray disc (BD), any other non-transitory optical medium,and/or the like. Such a non-volatile computer-readable storage mediummay also include read-only memory (ROM), programmable read-only memory(PROM), erasable programmable read-only memory (EPROM), electricallyerasable programmable read-only memory (EEPROM), flash memory (e.g.,Serial, NAND, NOR, and/or the like), multimedia memory cards (MMC),secure digital (SD) memory cards, SmartMedia cards, CompactFlash (CF)cards, Memory Sticks, and/or the like. Further, a non-volatilecomputer-readable storage medium may also include conductive-bridgingrandom access memory (CBRAM), phase-change random access memory (PRAM),ferroelectric random-access memory (FeRAM), non-volatile random-accessmemory (NVRAM), magnetoresistive random-access memory (MRAM), resistiverandom-access memory (RRAM), Silicon-Oxide-Nitride-Oxide-Silicon memory(SONOS), floating junction gate random access memory (FJG RAM),Millipede memory, racetrack memory, and/or the like.

In one embodiment, a volatile computer-readable storage medium mayinclude random access memory (RAM), dynamic random access memory (DRAM),static random access memory (SRAM), fast page mode dynamic random accessmemory (FPM DRAM), extended data-out dynamic random access memory (EDODRAM), synchronous dynamic random access memory (SDRAM), double datarate synchronous dynamic random access memory (DDR SDRAM), double datarate type two synchronous dynamic random access memory (DDR2 SDRAM),double data rate type three synchronous dynamic random access memory(DDR3 SDRAM), Rambus dynamic random access memory (RDRAM), TwinTransistor RAM (TTRAM), Thyristor RAM (T-RAM), Zero-capacitor (Z-RAM),Rambus in-line memory module (RIMM), dual in-line memory module (DEVIM),single in-line memory module (SIMM), video random access memory (VRAM),cache memory (including various levels), flash memory, register memory,and/or the like. It will be appreciated that where embodiments aredescribed to use a computer-readable storage medium, other types ofcomputer-readable storage media may be substituted for or used inaddition to the computer-readable storage media described above.

As should be appreciated, various embodiments of the present inventionmay also be implemented as methods, apparatus, systems, computingdevices, computing entities, and/or the like. As such, embodiments ofthe present invention may take the form of a data structure, apparatus,system, computing device, computing entity, and/or the like executinginstructions stored on a computer-readable storage medium to performcertain steps or operations. Thus, embodiments of the present inventionmay also take the form of an entirely hardware embodiment, an entirelycomputer program product embodiment, and/or an embodiment that comprisescombination of computer program products and hardware performing certainsteps or operations.

Embodiments of the present invention are described below with referenceto block diagrams and flowchart illustrations. Thus, it should beunderstood that each block of the block diagrams and flowchartillustrations may be implemented in the form of a computer programproduct, an entirely hardware embodiment, a combination of hardware andcomputer program products, and/or apparatus, systems, computing devices,computing entities, and/or the like carrying out instructions,operations, steps, and similar words used interchangeably (e.g., theexecutable instructions, instructions for execution, program code,and/or the like) on a computer-readable storage medium for execution.For example, retrieval, loading, and execution of code may be performedsequentially such that one instruction is retrieved, loaded, andexecuted at a time. In some exemplary embodiments, retrieval, loading,and/or execution may be performed in parallel such that multipleinstructions are retrieved, loaded, and/or executed together. Thus, suchembodiments can produce specifically-configured machines performing thesteps or operations specified in the block diagrams and flowchartillustrations. Accordingly, the block diagrams and flowchartillustrations support various combinations of embodiments for performingthe specified instructions, operations, or steps.

II. Exemplary System Architecture

FIG. 1 provides an illustration of a recovery prediction system 100 thatcan be used in conjunction with various embodiments of the presentinvention. As shown in FIG. 1, the recovery prediction system 100 maycomprise one or more analytic computing entities 65, one or more usercomputing entities 30, one or more networks 135, and/or the like. Eachof the components of the system may be in electronic communication with,for example, one another over the same or different wireless or wirednetworks 135 including, for example, a wired or wireless Personal AreaNetwork (PAN), Local Area Network (LAN), Metropolitan Area Network(MAN), Wide Area Network (WAN), and/or the like. Additionally, whileFIG. 1 illustrate certain system entities as separate, standaloneentities, the various embodiments are not limited to this particulararchitecture.

a. Exemplary Analytic Computing Entity

FIG. 2A provides a schematic of an analytic computing entity 65according to one embodiment of the present invention. In general, theterms computing entity, entity, device, system, and/or similar wordsused herein interchangeably may refer to, for example, one or morecomputers, computing entities, desktop computers, mobile phones,tablets, phablets, notebooks, laptops, distributed systems,items/devices, terminals, servers or server networks, blades, gateways,switches, processing devices, processing entities, set-top boxes,relays, routers, network access points, base stations, the like, and/orany combination of devices or entities adapted to perform the functions,operations, and/or processes described herein. Such functions,operations, and/or processes may include, for example, transmitting,receiving, operating on, processing, displaying, storing, determining,creating/generating, monitoring, evaluating, comparing, and/or similarterms used herein interchangeably. In one embodiment, these functions,operations, and/or processes can be performed on data, content,information, and/or similar terms used herein interchangeably.

As indicated, in one embodiment, the analytic computing entity 65 mayalso include one or more network and/or communications interfaces 208for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. For instance, theanalytic computing entity 65 may communicate with other computingentities 65, one or more user computing entities 30, and/or the like.

As shown in FIG. 2A, in one embodiment, the analytic computing entity 65may include or be in communication with one or more processing elements205 (also referred to as processors, processing circuitry, and/orsimilar terms used herein interchangeably) that communicate with otherelements within the analytic computing entity 65 via a bus, for example,or network connection. As will be understood, the processing element 205may be embodied in a number of different ways. For example, theprocessing element 205 may be embodied as one or more complexprogrammable logic devices (CPLDs), microprocessors, multi-coreprocessors, coprocessing entities, application-specific instruction-setprocessors (ASIPs), and/or controllers. Further, the processing element205 may be embodied as one or more other processing devices orcircuitry. The term circuitry may refer to an entirely hardwareembodiment or a combination of hardware and computer program products.Thus, the processing element 205 may be embodied as integrated circuits,application specific integrated circuits (ASICs), field programmablegate arrays (FPGAs), programmable logic arrays (PLAs), hardwareaccelerators, other circuitry, and/or the like. As will therefore beunderstood, the processing element 205 may be configured for aparticular use or configured to execute instructions stored in volatileor non-volatile media or otherwise accessible to the processing element205. As such, whether configured by hardware or computer programproducts, or by a combination thereof, the processing element 205 may becapable of performing steps or operations according to embodiments ofthe present invention when configured accordingly.

In one embodiment, the analytic computing entity 65 may further includeor be in communication with non-volatile media (also referred to asnon-volatile storage, memory, memory storage, memory circuitry and/orsimilar terms used herein interchangeably). In one embodiment, thenon-volatile storage or memory may include one or more non-volatilestorage or memory media 206 as described above, such as hard disks, ROM,PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, Memory Sticks,CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrack memory, and/or the like. Aswill be recognized, the non-volatile storage or memory media may storedatabases, database instances, database management system entities,data, applications, programs, program modules, scripts, source code,object code, byte code, compiled code, interpreted code, machine code,executable instructions, and/or the like. The term database, databaseinstance, database management system entity, and/or similar terms usedherein interchangeably and in a general sense to refer to a structuredor unstructured collection of information/data that is stored in acomputer-readable storage medium.

Memory media 206 may also be embodied as a data storage device ordevices, as a separate database server or servers, or as a combinationof data storage devices and separate database servers. Further, in someembodiments, memory media 206 may be embodied as a distributedrepository such that some of the stored information/data is storedcentrally in a location within the system and other information/data isstored in one or more remote locations. Alternatively, in someembodiments, the distributed repository may be distributed over aplurality of remote storage locations only. An example of theembodiments contemplated herein would include a cloud data storagesystem maintained by a third party provider and where some or all of theinformation/data required for the operation of the recovery predictionsystem may be stored. As a person of ordinary skill in the art wouldrecognize, the information/data required for the operation of therecovery prediction system may also be partially stored in the clouddata storage system and partially stored in a locally maintained datastorage system.

Memory media 206 may include information/data accessed and stored by therecovery prediction system to facilitate the operations of the system.More specifically, memory media 206 may encompass one or more datastores configured to store information/data usable in certainembodiments. For example, as shown in FIG. 2B, data stores encompassedwithin the memory media 206 may comprise provider information/data 211,recovery information/data 212, claim information/data 213, member data,and/or the like.

As illustrated in FIG. 2B, the data stores 206 may comprise providerinformation/data 211 having identifying information/data indicative ofvarious providers. For example, the provider information/data 211 maycomprise provider identifiers, provider locations, provider recoveryrates, and/or the like. The provider information/data may furthercomprise provider flag information/data providing an indicator conveyingthat the provider is involved in an ongoing investigation or may needthe provider's claims flagged for overpayment or fraud review.

Continuing with FIG. 2B, the data stores 206 may comprise recoveryinformation/data 212. The recovery information/data may comprise claimsthat have potentially recoverable amounts, recovery rates for providers,recovery rates for time periods, recovery rates for geographiclocations, and/or the like.

Continuing with FIG. 2B, claim information/data may comprise claiminformation/data 213 indicative of claims filed on behalf of a providerfor services or products. Examples of providers include medical doctors,nurse practitioners, physician assistants, nurses, other medicalprofessionals practicing in one or more of a plurality of medicalspecialties (e.g., psychiatry, pain management, anesthesiology, generalsurgery, emergency medicine, etc.), hospitals, urgent care centers,diagnostic laboratories, surgery centers, and/or the like. Moreover, theclaim information/data 213 may further comprise prescription claiminformation/data. Prescription claim information/data may be used toextract information/data such as the identity of entities that prescribecertain drugs and the pharmacies who fulfill such prescriptions.

The data stores 206 may further store member information/data 214 usedby the recovery prediction system. For example, member information/data212 stored by the data store may comprise member death information/dataindicative of the identity of members, such as their name, date ofbirth, date of death, and other identifying information/data such as asocial security number or member identification number. The memberinformation/data 212 may also comprise member flag information/dataindicative of the identity of various members that have been flaggedand/or otherwise identified as being of high risk and/or interest forclaim review for overpayment or fraud.

In one embodiment, the analytic computing entity 65 may further includeor be in communication with volatile media (also referred to as volatilestorage, memory, memory storage, memory circuitry and/or similar termsused herein interchangeably). In one embodiment, the volatile storage ormemory may also include one or more volatile storage or memory media 207as described above, such as RAM, DRAM, SRAM, FPM DRAM, EDO DRAM, SDRAM,DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DIMM, SIMM, VRAM, cachememory, register memory, and/or the like. As will be recognized, thevolatile storage or memory media may be used to store at least portionsof the databases, database instances, database management systementities, data, applications, programs, program modules, scripts, sourcecode, object code, byte code, compiled code, interpreted code, machinecode, executable instructions, and/or the like being executed by, forexample, the processing element 308. Thus, the databases, databaseinstances, database management system entities, data, applications,programs, program modules, scripts, source code, object code, byte code,compiled code, interpreted code, machine code, executable instructions,and/or the like may be used to control certain aspects of the operationof the analytic computing entity 65 with the assistance of theprocessing element 205 and operating system.

As indicated, in one embodiment, the analytic computing entity 65 mayalso include one or more network and/or communications interfaces 208for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. For instance, theanalytic computing entity 65 may communicate with computing entities orcommunication interfaces of other computing entities 65, user computingentities 30, and/or the like.

As indicated, in one embodiment, the analytic computing entity 65 mayalso include one or more network and/or communications interfaces 208for communicating with various computing entities, such as bycommunicating data, content, information, and/or similar terms usedherein interchangeably that can be transmitted, received, operated on,processed, displayed, stored, and/or the like. Such communication may beexecuted using a wired data transmission protocol, such as fiberdistributed data interface (FDDI), digital subscriber line (DSL),Ethernet, asynchronous transfer mode (ATM), frame relay, data over cableservice interface specification (DOCSIS), or any other wiredtransmission protocol. Similarly, the analytic computing entity 65 maybe configured to communicate via wireless external communicationnetworks using any of a variety of protocols, such as general packetradio service (GPRS), Universal Mobile Telecommunications System (UMTS),Code Division Multiple Access 2000 (CDMA2000), CDMA2000 1× (1×RTT),Wideband Code Division Multiple Access (WCDMA), Global System for MobileCommunications (GSM), Enhanced Data rates for GSM Evolution (EDGE), TimeDivision-Synchronous Code Division Multiple Access (TD-SCDMA), Long TermEvolution (LTE), Evolved Universal Terrestrial Radio Access Network(E-UTRAN), Evolution-Data Optimized (EVDO), High Speed Packet Access(HSPA), High-Speed Downlink Packet Access (HSDPA), IEEE 802.11 (Wi-Fi),Wi-Fi Direct, 802.16 (WiMAX), ultra-wideband (UWB), infrared (IR)protocols, near field communication (NFC) protocols, Wibree, Bluetoothprotocols, wireless universal serial bus (USB) protocols, and/or anyother wireless protocol. The analytic computing entity 65 may use suchprotocols and standards to communicate using Border Gateway Protocol(BGP), Dynamic Host Configuration Protocol (DHCP), Domain Name System(DNS), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP),HTTP over TLS/SSL/Secure, Internet Message Access Protocol (IMAP),Network Time Protocol (NTP), Simple Mail Transfer Protocol (SMTP),Telnet, Transport Layer Security (TLS), Secure Sockets Layer (SSL),Internet Protocol (IP), Transmission Control Protocol (TCP), UserDatagram Protocol (UDP), Datagram Congestion Control Protocol (DCCP),Stream Control Transmission Protocol (SCTP), HyperText Markup Language(HTML), and/or the like.

As will be appreciated, one or more of the analytic computing entity'scomponents may be located remotely from other analytic computing entity65 components, such as in a distributed system. Furthermore, one or moreof the components may be aggregated and additional components performingfunctions described herein may be included in the analytic computingentity 65. Thus, the analytic computing entity 65 can be adapted toaccommodate a variety of needs and circumstances.

b. Exemplary User Computing Entity

FIG. 3 provides an illustrative schematic representative of usercomputing entity 30 that can be used in conjunction with embodiments ofthe present invention. As will be recognized, the user computing entitymay be operated by an agent and include components and features similarto those described in conjunction with the analytic computing entity 65.Further, as shown in FIG. 3, the user computing entity may includeadditional components and features. For example, the user computingentity 30 can include an antenna 312, a transmitter 304 (e.g., radio), areceiver 306 (e.g., radio), and a processing element 308 that providessignals to and receives signals from the transmitter 304 and receiver306, respectively. The signals provided to and received from thetransmitter 304 and the receiver 306, respectively, may includesignaling information/data in accordance with an air interface standardof applicable wireless systems to communicate with various entities,such as an analytic computing entity 65, another user computing entity30, and/or the like. In this regard, the user computing entity 30 may becapable of operating with one or more air interface standards,communication protocols, modulation types, and access types. Moreparticularly, the user computing entity 30 may operate in accordancewith any of a number of wireless communication standards and protocols.In a particular embodiment, the user computing entity 30 may operate inaccordance with multiple wireless communication standards and protocols,such as GPRS, UMTS, CDMA2000, 1×RTT, WCDMA, TD-SCDMA, LTE, E-UTRAN,EVDO, HSPA, HSDPA, Wi-Fi, WiMAX, UWB, IR protocols, Bluetooth protocols,USB protocols, and/or any other wireless protocol.

Via these communication standards and protocols, the user computingentity 30 can communicate with various other entities using conceptssuch as Unstructured Supplementary Service data (USSD), Short MessageService (SMS), Multimedia Messaging Service (MMS), Dual-ToneMulti-Frequency Signaling (DTMF), and/or Subscriber Identity ModuleDialer (SIM dialer). The user computing entity 30 can also downloadchanges, add-ons, and updates, for instance, to its firmware, software(e.g., including executable instructions, applications, programmodules), and operating system.

According to one embodiment, the user computing entity 30 may includelocation determining aspects, devices, modules, functionalities, and/orsimilar words used herein interchangeably. For example, the usercomputing entity 30 may include outdoor positioning aspects, such as alocation module adapted to acquire, for example, latitude, longitude,altitude, geocode, course, direction, heading, speed, UTC, date, and/orvarious other information/data. In one embodiment, the location modulecan acquire data, sometimes known as ephemeris data, by identifying thenumber of satellites in view and the relative positions of thosesatellites. The satellites may be a variety of different satellites,including LEO satellite systems, DOD satellite systems, the EuropeanUnion Galileo positioning systems, the Chinese Compass navigationsystems, Indian Regional Navigational satellite systems, and/or thelike. Alternatively, the location information/data/data may bedetermined by triangulating the position in connection with a variety ofother systems, including cellular towers, Wi-Fi access points, and/orthe like. Similarly, the user computing entity 30 may include indoorpositioning aspects, such as a location module adapted to acquire, forexample, latitude, longitude, altitude, geocode, course, direction,heading, speed, time, date, and/or various other information/data. Someof the indoor aspects may use various position or location technologiesincluding RFID tags, indoor beacons or transmitters, Wi-Fi accesspoints, cellular towers, nearby computing devices (e.g., smartphones,laptops) and/or the like. For instance, such technologies may includeiBeacons, Gimbal proximity beacons, BLE transmitters, Near FieldCommunication (NFC) transmitters, and/or the like. These indoorpositioning aspects can be used in a variety of settings to determinethe location of someone or something to within inches or centimeters.

The user computing entity 30 may also comprise a user interfacecomprising one or more user input/output interfaces (e.g., a display 316and/or speaker/speaker driver coupled to a processing element 308 and atouch screen, keyboard, mouse, and/or microphone coupled to a processingelement 308). For example, the user output interface may be configuredto provide an application, browser, user interface, dashboard, webpage,and/or similar words used herein interchangeably executing on and/oraccessible via the user computing entity 30 to cause display or audiblepresentation of information/data and for user interaction therewith viaone or more user input interfaces. The user output interface may beupdated dynamically from communication with the analytic computingentity 65. The user input interface can comprise any of a number ofdevices allowing the user computing entity 30 to receive data, such as akeypad 318 (hard or soft), a touch display, voice/speech or motioninterfaces, scanners, readers, or other input device. In embodimentsincluding a keypad 318, the keypad 318 can include (or cause display of)the conventional numeric (0-9) and related keys (#, *), and other keysused for operating the user computing entity 30 and may include a fullset of alphabetic keys or set of keys that may be activated to provide afull set of alphanumeric keys. In addition to providing input, the userinput interface can be used, for example, to activate or deactivatecertain functions, such as screen savers and/or sleep modes. Throughsuch inputs the user computing entity 30 can collect information/data,user interaction/input, and/or the like.

The user computing entity 30 can also include volatile storage or memory322 and/or non-volatile storage or memory 324, which can be embeddedand/or may be removable. For example, the non-volatile memory may beROM, PROM, EPROM, EEPROM, flash memory, MMCs, SD memory cards, MemorySticks, CBRAM, PRAM, FeRAM, RRAM, SONOS, racetrack memory, and/or thelike. The volatile memory may be RAM, DRAM, SRAM, FPM DRAM, EDO DRAM,SDRAM, DDR SDRAM, DDR2 SDRAM, DDR3 SDRAM, RDRAM, RIMM, DEVIM, SIMM,VRAM, cache memory, register memory, and/or the like. The volatile andnon-volatile storage or memory can store databases, database instances,database management system entities, data, applications, programs,program modules, scripts, source code, object code, byte code, compiledcode, interpreted code, machine code, executable instructions, and/orthe like to implement the functions of the user computing entity 30.

c. Exemplary Networks

In one embodiment, the networks 135 may include, but are not limited to,any one or a combination of different types of suitable communicationsnetworks such as, for example, cable networks, public networks (e.g.,the Internet), private networks (e.g., frame-relay networks), wirelessnetworks, cellular networks, telephone networks (e.g., a public switchedtelephone network), or any other suitable private and/or publicnetworks. Further, the networks 135 may have any suitable communicationrange associated therewith and may include, for example, global networks(e.g., the Internet), MANs, WANs, LANs, or PANs. In addition, thenetworks 135 may include any type of medium over which network trafficmay be carried including, but not limited to, coaxial cable,twisted-pair wire, optical fiber, a hybrid fiber coaxial (HFC) medium,microwave terrestrial transceivers, radio frequency communicationmediums, satellite communication mediums, or any combination thereof, aswell as a variety of network devices and computing platforms provided bynetwork providers or other entities.

III. Exemplary System Operation

Reference will now be made to FIGS. 4A, 4B, 4C, 4D, 5, and 6. FIGS.4A-4D provide exemplary features and/or feature sets. FIG. 5 provides aflowchart of operations and/or processes for carrying out embodiments ofthe present invention. And FIG. 6 provides an exemplary interface thatcan be generated and/or populated using machine learning.

a. Brief Overview

As indicated, there is a latent need for a rigorous methodology that canpredict overpaid claims with the highest likelihood of recovery,determine priority scores based at least in part on the same, anddynamically present the prioritized results via a user interface usingboth time-dependent factors and time-independent factors.

1. Technical Problem

In some instances, recovery agents (e.g., human recovery agents) may beconsidered a scarce resource. Because of this, a technical approach forprioritizing overpaid claims (referred to as claim inventory, recoveryinventory, open inventory, open claim inventory, and/or or similar termsused herein interchangeably) to maximize claim recoveries is needed(e.g., recoveries of overpaid claims). Existing solutions simplyprioritize and present inventory based on the total amount recoverable.In other words, claim inventory is simply ordered by the largest amountowed and presented via a user interface in that order to be handledaccordingly. This human-driven approach has significant drawbacks inthat it does not consider or predict the likelihood that an overpaidamount will actually be recovered. Moreover, this human-driven approachdoes not determine and/or contemplate time-independent andtime-dependent features from the available information/data because itis not practical or feasible for a human-implementable system tomanually incorporate these factors and/or to continuouslyprioritize/reprioritize the inventory.

2. Technical Solution

To overcome at least the above-identified technical challenges, machinelearning can be used in a continuously learning manner to determine thelikelihood of recovery for each claim in inventory and prioritize theinventory using a unique approach for presentation via a dynamicallyupdatable user interface. As will be recognized, humans simply do nothave the ability resolve all of the hidden correlations in thetime-independent information/data and the time-dependentinformation/data. And even if that were somehow possible, any suchderivations would be out-of-date by the amount of time that would berequired to deploy any human-implementable solution. By using machinelearning, though, vast amounts of time-independent information/data andthe time-dependent information/data can be continuously analyzed toadapt to, for example, changing legislation, operations, andenvironments to quickly deploy a solution that is accurate, up-to-date,and computationally efficient to continuously deploy models that areaccurate and responsive to the dynamic conditions. This disclosuredescribes a machine learning approach that can analyze vast amounts ofinformation/data in a computational efficient manner to train one ormore machine learning models, use the one or more machine learningmodels to determine predictions for the claim inventory, determinepriority scores for the claim inventory based on the predictions,prioritize the inventory, and dynamically update a user interface.

b. Recovery Agents

In one embodiment, a user (such as a recovery agent) may navigate a userinterface 600 by operating a user computing entity 30. Through the userinterface 600, the user (e.g., recovery agent) may view and access claiminventory, claim information/data, member information/data, providerinformation/data, recovery information/data, and/or the like. To do so,the recovery prediction system 100 may provide access to the system viaa user profile that has been previously established and/or stored. In anexample embodiment, a user profile comprises user profileinformation/data, such as a user identifier configured to uniquelyidentify the user, a username, user contact information/data (e.g.,name, one or more electronic addresses such as emails, instant messageusernames, social media user name, and/or the like), user preferences,user account information/data, user credentials, information/dataidentifying one or more user computing entities 30 corresponding to theuser, and/or the like.

In various embodiments, the user profile may be associated with one ormore queues assigned to the user (e.g., recovery agent). The queues canbe updated continuously, regularly, and/or in response to certaintriggers. Moreover, the queues may be any of a variety of datastructures that allow for efficient and dynamic prioritization andreprioritization, such as array data structures, heap data structures,map data structures, linked list data structures, tree data structures,and/or the like. Dynamically updating a queue associated with aparticular user (e.g., recovery agent) can cause an active userinterface 600 with which the user is interacting to automatically beupdated.

In other embodiments, a recovery agent may be an artificial recoveryagent, such as artificial intelligence (AI) bots that can perform atleast some or a subset of the functions of a human recovery agent. Insuch an embodiment, each artificial recovery agent can be associatedwith one or more queues and benefit from the techniques and approachesdescribed herein.

c. Feature Processing

In various embodiments, whether training a machine learning model ordetermining predictions for open inventory, claim information/data canbe accessed or received in a variety of manners (e.g., step/operation500 of FIG. 5). Then, to train a machine learning model, features setsare identified or extracted from the claim information/data. As will berecognized, claim information/data may comprise various types ofinformation/data. FIGS. 4A, 4B, 4C, and 4D represent at least portionsof feature sets that can identified or extracted from the claiminformation/data.

For context in understanding embodiments of the present invention, whena claim is received by a claims system (not shown), the claim representsa request for payment/reimbursement for services rendered, materialsused, equipment provided, and/or the like. For example, a claim may be arequest for payment/reimbursement for a consultation with a primary caredoctor, a medical procedure or an evaluation performed by an orthopedicsurgeon, a laboratory test performed by a laboratory, a surgery, durablemedical equipment provided to an injured patient, medications or othermaterials used in the treatment of a patient, and/or the like. As willbe recognized, though, embodiments of the present invention are notlimited to the medical context. Rather, they may be applied to a varietyof other settings, such as automotive claims, repair claims, and/or thelike.

In one embodiment, each claim may be stored as a record that comprises atextual description of the type of claim to which the record correspondsand comprises member features (FIG. 4A), claim features (FIG. 4B),provider features (FIG. 4C), and/or recovery features (4D). The variousfeatures and feature sets can be identified in a manual, semi-automatic,and/or automatic manner for identification and/or extraction for a givenclaim (operation/step 502 of FIG. 5).

FIG. 4A provides a subset of member features that can be associated witha given member, provider, claim, and/or recovery. As used herein, theterm member may refer to a person who receives healthcare services orproducts rendered by a provider and/or who relies on financing from ahealth insurance payer to cover the costs of the rendered healthservices or products. In that sense, a member is associated with thehealth insurance payer and is said to be a member of (a programassociated with) of the health insurance payer. In one embodiment,member features can include, but are not limited to, age, gender,poverty rates, known health conditions, home location, profession,access to medical care, medical history, claim history, memberidentifier (ID), and/or the like.

FIG. 4B provides a subset of claim features that can be associated witha given member, provider, claim, and/or recovery. The claims featuresmay continuously change (e.g., be time-dependent) for many reasons, suchas the prevalence of certain diseases, the emergence of new diseases(e.g., representing new claim), and/or previous medical codes beingintroduced and/or discontinued.

Example claim features may include a claim ID and the date a claim wasreceived—e.g., Dec. 14, 2013, at 12:00:00 pm and time stamped as2013-12-14 12:00:00. The claim features may also include one or morediagnostic codes, treatment codes, treatment modifier codes, and/or thelike. Such codes may be any code, such as Current Procedural Terminology(CPT) codes, billing codes, Healthcare Common Procedure Coding System(HCPCS) codes, ICD-10-CM Medical Diagnosis Codes, and/or the like.

By way of example of billing codes, a patient may visit a doctor becauseof discomfort in his lower leg. During the visit, the doctor may examinethe patient's lower leg and take an x-ray of the lower leg as part of anexamination. The claim for the visit may have two distinct billingcodes: billing code 99213 and billing code 73590. Billing code 99213 maybe used to request payment/reimbursement for the visit, examination, andevaluation of the patient. Billing code 73590 may be used to requestpayment/reimbursement for the x-ray of the leg. Using such codes andcode sets, various correlations can be determined as they related torecoverability.

FIG. 4C provides a subset of provider features that can be associatedwith a given member, provider, claim, and/or recovery. A provider mayrefer to an entity that provides services or products. In at least oneembodiment, in the health care context, providers rely on a healthinsurance payers to finance or reimburse the cost of the services orproducts provided. For example, a provider may comprise a healthprofessional operating within one or more of a plurality of branches ofhealthcare, including medicine, surgery, dentistry, midwifery, pharmacy,psychology, psychiatry, pain management, nursing, laboratorydiagnostics, and/or the like. A provider may also comprise anorganization, such as a private company, hospital, laboratory, or thelike, that operates within one or more of a plurality of branches ofhealthcare. Each provider may be associated with provider features thatincludes, but not are not limited to, demographics (e.g., the locationin which the provider operations), contracted status, specialty, and/orone or more recovery rates for the provider. A recovery rate mayidentify that recovery rate of overpayments to the provider over one ormore time periods or epochs (e.g., 1 month, 3 months, 6 months, 12months, and/or the like).

Similar to claim features, provider features can continuously change(e.g., be time-dependent) for several reasons. For instance, within agiven provider, the software, policies for submitting claims, personnel,strategies for submitting claims, experience, and/or the like may changein an unpredictable manner and result in a sudden change to therecoverability associated with that provider.

FIG. 4D provides a subset of recovery features that can be associatedwith a given member, provider, claim, and/or recovery. As will berecognized, if a claim is in inventory, the claim has been adjudicated,paid, and identified as being overpaid (e.g., being associated with apotentially recoverable amount). The recovery features may include, butare not limited to, a potentially recoverable amount (e.g.,overpayment), an overpayment reason, a recovered amount, and a recoveryrate. With regard to recovery rates, each claim may be associated with arecovery rate (e.g., a claim recovery rate), and each provider may beassociated with one or more separate recovery rates (e.g., provider arecovery rate).

As will be recognized, the recovery features can continuously change,both for specific providers and for the provider population in general.For example, this can result from new overpayment reasons beingidentified or old ones becoming less relevant. The frequency ofoccurrence of overpayment reasons can increase or decrease depending ona variety of factors. Such factors include contract renewals,adjudication software changes, claim processing and submission softwarechanges, and/or the like. Since the recoverability may be impacted bythe overpayment reason, continuous and unpredictable changes tooverpayment reasons can impact recoverability in real-time.

As will be recognized, the member features, claim features, providerfeatures, and recovery features can be used to manually,semi-automatically, or automatically establish, update, and/or modifyfeature sets. A feature set comprises one or more features from themember features, claim features, provider features, and/or recoveryfeatures.

Determine a Recovery Success Rate for Each Claim

As indicated in step/operation 504 of FIG. 5, the recovery predictionsystem 100 (e.g., via an analytic computing entity 65) can receive a setof claims that has been adjudicated, paid, and had at least a portion ofan overpaid amount recovered (e.g., a recovered claim). Using the set ofclaims, the recovery prediction system 100 (e.g., via an analyticcomputing entity 65) can determine a recovery success rate for eachclaim. To do so, the recovery prediction system 100 (e.g., via ananalytic computing entity 65) identifies or extracts the appropriatefeatures for each claim. The recovery prediction system 100 (e.g., viaan analytic computing entity 65) can then determine the recovery successrate for each claim using the following formula a=x/y. In the formula,the recovery success rate for a given claim is the amount recovereddivided by the amount owed. Thus, the quotient a represents the recoverysuccess rate, the dividend x represents the recovered amount, and thedivisor y represents the amount owed (or the potentially recoverableamount). Table 1 below provides an example of a recovered claim.

TABLE 1 Claim a y = Amount Owed: $100 x = Amount Recovered: $80 a =Recovery Success Rate: .80

The recovery success rate can be determined for each claim. The recoveryprediction system 100 (e.g., via an analytic computing entity 65) canuse such information/data as a training dataset and/or across-validation (or test) dataset—with the target output or variablebeing the recovery success rate. The recovery success rate can also beused to trigger retraining of one or more machine learning models.

e. Train Machine Learning Model

As previously indicated, embodiments of the present invention use one ormore machine learning models to predict a recovery score for each unseenclaim in open inventory. Thus, as will be recognized, training one ormore machine learning algorithms to generate one or more machinelearning models involves providing a training dataset to the machinelearning algorithms (step/operation 506 of FIG. 5). The training datasetcontains the target output or variable that the machine-learning modelis to eventually predict. The machine-learning algorithm detectspatterns in the training dataset that map the input information/dataattributes from the feature sets to the target output or variable andcaptures these patterns. The resulting machine-learning model is thenable to generate predictions for new or unseen information/data forwhich the target is unknown. The predictions can be determined inreal-time—such as when a new claim is received in inventory. Byperforming real-time predictions, a user interface for a given agent canalso be updated dynamically to provide the most up-to-date andprioritized claim information/data.

In one embodiment, the disclosed approach uses supervised machinelearning. With embodiments of the present invention, exemplary machinelearning algorithms may include general purpose classification orregression algorithms, such as random forest or random decision forestalgorithms and/or gradient boost algorithms. As will be recognized, avariety of different machine learning algorithms and techniques can beused within the scope of embodiments of the present invention.

As a result of the training, one or more machine learning models aregenerated to subsequently predict recovery scores of unseen claims. Forinstance, using the machine learning model, the recovery predictionsystem 100 (e.g., via an analytic computing entity 65) generatespredicted recovery rates/scores for unseen claims. As will also berecognized from the discussion regarding the feature sets, the describedmachine learning model is capable of ingesting numeric information/data(such as claim balance) and categorical information/data (such as amember or provider location).

As will be appreciated, the hidden and/or weak correlations found as aresult of the machine learning are simply not practical forhuman-implementation. In addition to outputting predicted recoveryrates/scores of unseen claims in inventory, the machine learning modelscan be updated on a continuous, regular, or triggered basis.

f. Score Open Inventory using Machine Learning Model

As indicated by step/operation 508, with one more machine learningmodels generated, the recovery prediction system 100 (e.g., via ananalytic computing entity 65) can use the one or more machine learningmodels to generate predicted recovery rates/scores for (e.g., score)unseen claims in open inventory. Unseen claims in open inventory areclaims that have been adjudicated, paid, and indicated as having someamount of overpayment.

In various embodiments, the open inventory of claims can be scored usinga variety of approaches. For example, in one embodiment, open inventorymay refer to all open inventory (at an inventory level), such that thescoring is performed in batch for all claims in inventory. In anotherembodiment, open inventory may refer to a subset of all open inventory(e.g., at a queue level assigned to a particular queue associated with auser), such that claims in a particular queue are scored together. Andin yet another embodiment, claims can be scored in real-time as they arereceived in open inventory (individually or in batch). Thus, responsiveto the recovery prediction system 100 (e.g., via an analytic computingentity 65) receiving one or more unseen claims, the recovery predictionsystem 100 (e.g., via an analytic computing entity 65) can score (e.g.,generate a predicted recovery rate/score for) the one or more unseenclaims. For context, Table 2 below provides example predicted recoveryrates/scores as output from the one or more machine learning models.This example includes 5 claims: claim a, claim b, claim c, claim d, andclaim e.

TABLE 2 Predicted Recovery Rates/Scores Claim a: 0.4 Claim b: 0.2 Claimc: 0.8 Claim d: 0.4 Claim e: 0.7

In these examples, each of the above claims has a predicted recoveryrate/score in the domain [0,1].

As will be recognized, the set of open inventory (e.g., unrecoveredclaims) is highly dynamic and changes continuously during a given timeperiod, such as a working day. Such changes are continuous due to thecontinuous nature of claim processing, overpayment identification, andrecovery payment operations. For example, a given claim that is part ofopen inventory at the beginning of a work day could be removed from openinventory a few minutes later once a recovered amount is recorded by therecovery prediction system 100 as having been received. Similarly, newclaims can appear in open inventory during a work day as overpaymentprocesses continuously execute. This necessitates a continuous scoring,prioritizing, and reprioritizing of open inventory (on an inventorylevel or a queue level).

The individually scored claims can also be assigned to one or morequeues before, during, and/or after scoring. Once assigned to aparticular queue, the corresponding user (e.g., recovery agent) has theability to access claim information/data through a user interface 600based at least in part on his or her access credentials. The claims canbe assigned to queues using a variety of techniques and approaches.

g. Prioritize Inventory

In one embodiment, the recovery prediction system 100 (e.g., via ananalytic computing entity 65) can prioritize the open inventory at aninventory level, a queue level, and/or the like. To prioritize a set ofclaims in open inventory, embodiments of the present invention use anon-conventional approach. For example, existing methodologies forprioritization typically stack rank claims based on the total amount due(e.g., the amount of overpayment). Using this conventional approach, therecovery prediction system 100 (e.g., via an analytic computing entity65) would simply order the claims based on the overpayment amounts.Continuing the above example, claims a, b, c, d, and e have respectiveoverpayments of a: $100, b: $1,000, c: $50, d: $20,000, e: $400. Usingthe conventional prioritization approach, the recovery prediction system100 (e.g., via an analytic computing entity 65) would prioritize theclaims as indicated below in Table 3.

TABLE 3 Conventional Ranking (d, b, e, a, c) Claim d: $20,000 Claim b:$1,000 Claim e: $400 Claim a: $100 Claim c: $50

However, conventional methodologies have shortcomings in that it they donot contemplate the likelihood of actually recovery of each claim. Thus,a new and non-conventional approach for prioritization is alsodisclosed. In this new and non-conventional approach (step/operation 510of FIG. 5), prioritization comprises at least two steps: (1) determininga priority score for each claim based at least in part on the amountowed and the predicted recovery rate/score, and (2) ordering the claimsbased at least in part on the same. In other words, the claims beingprioritized are ordered by the product of the predicted recoveryrate/score and the outstanding claim balance: a=yx. In this equation, ais the product (or priority number) with y representing the machinelearning model's predicted recovery rate/score and x representing thepotentially recoverable amount.

With the same example claims a, b, c, d, and e, this new andnon-conventional approach would determine a different priority orderthan conventional methodologies. The respective overpayments of theseclaims are a: $100, b: $1,000, c: $50, d: $20,000, e: $400, and thepredicted recovery rates/scores for the claims are a: 0.4, b: 0.2, c:0.8, d: 0.4, e: 0.7. Thus, using the equation a=yx, the correspondingproducts are a: 40, b: 200, c: 40, d: 8000, e: 280. The priority rankingusing this non-conventional approach would claim d, claim e, claim b,claim a, claim c as indicated below in Table 4.

TABLE 4 Non-Conventional Ranking (d, e, b, a, c) Claim d: 8000 Claim e:280 Claim b: 200 Claim a: 40 Claim c: 40

Accordingly, the open inventory priority—whether at an inventory levelor a queue level—contemplates both the potentially recoverable amountand the likelihood of recovery. As such, the inventory would beprioritized in the order the following order: d, e, b, a, c.

In one embodiment, a variety of techniques and approaches can be used tosort/order and/or the store the claims based on their priority. In oneembodiment, each claim comprises an indication of its priority score.Because of how the priority scores are determined, this score isindependent of other claims and therefore can follow each claim. Thus,if claims are provided to users (e.g., recovery agents) in anenvironment in which the next available agent is pushed the next highestpriority claim, claim d would be the first claim pushed to a userinterface 600 for the next available user for handling. However, in anenvironment in which each user is assigned a queue of claims, the user'suser interface 600 would be dynamically updated to reflect the claim inpriority order. In this example, the queue can be resorted each time anew claim is added, or the user interface 600 may be configured todisplay a set of claims based at least on their priority scores. As willbe recognized, a variety of other approaches and techniques can be usedto adapt to various needs and circumstances. For example, the recoveryprediction system 100 (e.g., via an analytic computing entity 65) maycontinuously determine predicted recovery rates/scores for unseenclaims, continuously determine priority scores after determining thepredicted recovery rates/scores, continuously add or inserts the unseenclaims to one or more queues, prioritize the claims, and continuouslypush the updates to one or more user interfaces 600 in parallel.

h. Dynamically Update User Interface

As indicated above, a queue assigned to a particular user can beprovided by the recovery prediction system 100 (e.g., via an analyticcomputing entity 65) for accessing, viewing, investigating, and/ornavigating via a user interface 600 being displayed by a user computingentity 30. Thus, the user interface 600 can be dynamically updated toshow the most current priority order of claims, for example, assigned toa user (e.g., recovery agent) at any given time (step/operation 512 ofFIG. 5). For instance, if a claim in a queue is resolved and has apayment applied, the recovery prediction system 100 (e.g., via ananalytic computing entity 65) can push an update to the correspondingqueue and updating the priority order of the queue. In anotherembodiment, the user interface 600 may dynamically update the queuebeing displayed on a continuous or regular basis or in response tocertain triggers.

As shown via the user interface 600 of FIG. 6, the user interface maycomprise various features and functionality for accessing, viewing,investigating, and/or navigating claims in open inventory or in a queue.In one embodiment, the user interface 600 may identify the user (e.g.,recovery agent) credentialed for currently accessing the user interface600 (e.g., John Doe). The user interface 600 may also comprise messagesto the user in the form of banners, headers, notifications, and/or thelike.

In one embodiment, the user interface 600 may display one or more claimcategory elements 605A-605N. The terms elements, indicators, graphics,icons, images, buttons, selectors, and/or the like are used hereininterchangeably. In one embodiment, the claim category elements605A-605N may represent respective queues assigned to a credentialeduser. For example, claim category element 605A may represent a firstqueue assigned to a user, claim category element 605B may represent asecond queue assigned to the user, and so on. In another embodiment, theclaim category element 605A-605N may represent portions of a singlequeue assigned to the user based on threshold amounts. For example, theclaim category element 605A may represent claims having a priority scorewithin a first threshold, the claim category element 605B may representclaims having a priority score within a second threshold, and so on. Inyet another embodiment, the claim category elements 605A-605N maycomprise all of the claims in open inventory and allow for reviewing thestatus of claims within particular thresholds. In one embodiment, eachclaim category element 605A-605N may be selected to control what theuser interface 600 displays as the information/data in elements 615,620, 625, 630, 635, 640, 645, 650, 655, and/or the like. For example, ifclaim category element 605A is selected via a user computing entity 30,elements 615, 620, 625, 630, 635, 640, 645, 650, and 655 are dynamicallypopulated with information/data corresponding to category 5 claims.

In one embodiment, each claim category element 605A-605N may further beassociated with category sort elements 610A-610N. The category sortelements 610A-610N may be selected via the user computing entity 30 tocontrol how the user interface 600 sorts and displays theinformation/data in elements 615, 620, 625, 630, 635, 640, 645, 650,655, and/or the like.

In one embodiment, elements 615, 620, 625, 630, 635, 640, 645, 650, 655,and/or the like may comprise claims (and at least a portion of theircorresponding information/data) for a particular category. For example,element 615 may be selectable for sorting and represent the category ofclaims selected via a claim category element 605A-605N. Elements 620 and625 may be selectable elements for sorting and represent minimum andmaximum dates the claims were submitted, processed, and/or flagged foroverpayment. Element 630 may be selectable for sorting and represent theID of the claim, the ID of a provider who submitted the claim, the ID ofa member to whom the claim corresponds, a tax identification number of aprovider, and/or the like. Elements 635 and 640 may be selectable forsorting and represent location information for the corresponding claimline. And elements 645, 650, and 650 may be selectable for sorting andrespectively represent the priority score, the amounts overpaid, thepredicted recovery rate/score, and/or the like of the claims beingdisplayed. As will be recognized, the described elements are providedfor illustrative purposes and are not to be construed as limiting thedynamically updatable interface in any way. As indicated above, the userinterface 600 can be dynamically updated to show the most currentpriority order of claims at an inventory level, a queue level, and/orthe like.

i. Retraining

In one embodiment, the recovery prediction system 100 continuouslyretrains the models to adapt to changes in claim features, memberfeatures, provider features, recovery features, and/or the like(step/operation 514 of FIG. 5). For example, a given provider whopreviously had a relatively good recovery rate may suddenly have a poorrecovery rate. Such a change may be due to time-dependent factors (e.g.,due to a change in policy) and/or time-independent factors. As a result,the predicted recovery rates/scores determined by the one or moremachine learning models become stale. Thus, the recovery predictionsystem 100 continuously retrains the machine learning models by rapidlygenerating the new feature sets and determining recovery success ratesfrom new claims for retraining.

As will be recognized, the retraining may be initiated in a variety ofways. In one embodiment, the recovery prediction system 100 can retrainthe one or more machine learning models on a fixed schedule (e.g.,hourly, daily, weekly, and/or the like). In another embodiment, therecovery prediction system 100 can retrain the one or more machinelearning models based at least in part on one or more automatedtriggers. For example, the recovery prediction system 100 may executechange detection algorithms to detect changes in claim features,provider features, member features, and/or recovery features. Examplesof such changes may be the discontinued use of particular codes, use ofnew codes, changes in recovery rates for providers, changes in recoveryrates for types of claims, changes in particular distributions, newlocations being services, types of plans being accepted, and/or thelike.

VI. Conclusion

Many modifications and other embodiments of the inventions set forthherein will come to mind to one skilled in the art to which theseinventions pertain having the benefit of the teachings presented in theforegoing descriptions and the associated drawings. Therefore, it is tobe understood that the inventions are not to be limited to the specificembodiments disclosed and that modifications and other embodiments areintended to be included within the scope of the appended claims.Although specific terms are employed herein, they are used in a genericand descriptive sense only and not for purposes of limitation.

1. A computer-implemented method for dynamically updating a userinterface, the method comprising: receiving, by a system, a plurality ofclaims in real-time for electronic storage in an open claim inventory,the plurality of claims comprising a first claim and a second claim;determining, by the system and using one or more machine learningmodels, a first predicted claim score for the first claim and a secondpredicted claim score for the second claim, wherein each predicted claimscore is indicative of a predictive measure for the corresponding claim;determining, by the system, a first priority score for the first claimand a second priority score for the second claim, wherein each priorityscore is determined based at least in part on the correspondingpredicted claim score and a corresponding claim feature; inserting, bythe system, the first claim in a first queue data structure, wherein thefirst queue data structure (a) is associated with a first plurality ofclaims, and (b) comprises an indication of the first priority score forthe first claim and respective priority scores for the first pluralityof claims; inserting, by the system, the second claim in a second queuedata structure, wherein the second queue data structure (a) isassociated with a second plurality of claims, and (b) comprises anindication of the second priority score for the second claim andrespective priority scores for the second plurality of claims;prioritizing, by the system, (a) the first claim and the first pluralityof claims in the first queue data structure, and (b) the second claimand the second plurality of claims in the second queue data structure;and dynamically providing, by the system, an update to (a) a first userinterface element associated with the first queue data structure, and(b) a second user interface element associated with the second queuedata structure, wherein the user interface displays the update (a) tothe first user interface element, and (b) the second user interfaceelement.
 2. The computer-implemented method of claim 1 furthercomprising: extracting a first feature set from a set of trainingclaims; determining a claim-related rate for each claim of the set oftraining claims, wherein the claim-related rate is a target output ofthe one or more machine learning models; and storing the first featureset and the claim-related rates for the set of training claims as atraining dataset.
 3. The computer-implemented method of claim 2 furthercomprising automatically detecting a change in the first feature set andthe first claim.
 4. The computer-implemented method of claim 2 furthercomprising training the one or more machine learning models based atleast in part on the training dataset.
 5. The computer-implementedmethod of claim 1, wherein the priority score for the first claim is theproduct of the predicted claim score for the first claim and a firstrecoverable amount for the first claim.
 6. The computer-implementedmethod of claim 1, wherein dynamically providing the update to the firstuser interface element and the second user interface element comprisespushing the update.
 7. The computer-implemented method of claim 1further comprising: determining a predicted claim score for each of thefirst plurality of claims, and determining a priority score for each ofthe first plurality of claims.
 8. A computer program product comprisinga non-transitory computer readable medium having computer programinstructions stored therein, the computer program instructions whenexecuted by a processor, cause the processor to: receive a plurality ofclaims in real-time for electronic storage in an open claim inventory,the plurality of claims comprising a first claim and a second claim;determine a first predicted claim score for the first claim and a secondpredicted claim score for the second claim using one or more machinelearning models, wherein each predicted claim score is indicative of apredictive measure for the corresponding claim; determine a firstpriority score for the first claim and a second priority score for thesecond claim, wherein each priority score is determined based at leastin part on the corresponding predicted claim score and a correspondingclaim feature; insert the first claim in a first queue data structure,wherein the first queue data structure (a) is associated with a firstplurality of claims, and (b) comprises an indication of the firstpriority score for the first claim and respective priority scores forthe first plurality of claims; insert, by the system, the second claimin a second queue data structure, wherein the second queue datastructure (a) is associated with a second plurality of claims, and (b)comprises an indication of the second priority score for the secondclaim and respective priority scores for the second plurality of claims;prioritize (a) the first claim and the first plurality of claims in thefirst queue data structure, and (b) the second claim and the secondplurality of claims in the second queue data structure; and dynamicallyprovide an update to (a) a first user interface element associated withthe first queue data structure, and (b) a second user interface elementassociated with the second queue data structure, wherein the userinterface displays the update (a) to the first user interface element,and (b) the second user interface element.
 9. The non-transitorycomputer readable medium of claim 8, wherein the computer programinstructions when executed by a processor, further cause the processorto: extract a first feature set from a set of training claims; determinea claim-related rate for each claim of the set of training claims,wherein the claim-related rate is a target output of the one or moremachine learning models; and store the first feature set and theclaim-related rates for the set of training claims as a trainingdataset.
 10. The non-transitory computer readable medium of claim 9,wherein the computer program instructions when executed by a processor,further cause the processor to automatically detect a change in thefirst feature set and the first claim.
 11. The non-transitory computerreadable medium of claim 9, wherein the computer program instructionswhen executed by a processor, further cause the processor to train theone or more machine learning models based at least in part on thetraining dataset.
 12. The non-transitory computer readable medium ofclaim 8, wherein the priority score for the first claim is the productof the predicted claim score for the first claim and a first recoverableamount for the first claim.
 13. The non-transitory computer readablemedium of claim 8, wherein dynamically providing the update to the firstuser interface element and the second user interface element comprisespushing the update.
 14. The non-transitory computer readable medium ofclaim 8, wherein the computer program instructions when executed by aprocessor, further cause the processor to: determine a predicted claimscore for each of the first plurality of claims; and determine apriority score for each of the first plurality of claims.
 15. Acomputing system comprising a non-transitory computer readable storagemedium and one or more processors, the computing system configured to:receive a plurality of claims in real-time for electronic storage in anopen claim inventory, the plurality of claims comprising a first claimand a second claim; determine a first predicted claim score for thefirst claim and a second predicted claim score for the second claimusing one or more machine learning models, wherein each predicted claimscore is indicative of a predictive measure for the corresponding claim;determine a first priority score for the first claim and a secondpriority score for the second claim, wherein each priority score isdetermined based at least in part on the corresponding predicted claimscore and a corresponding claim feature; insert the first claim in afirst queue data structure, wherein the first queue data structure (a)is associated with a first plurality of claims, and (b) comprises anindication of the first priority score for the first claim andrespective priority scores for the first plurality of claims; insert, bythe system, the second claim in a second queue data structure, whereinthe second queue data structure (a) is associated with a secondplurality of claims, and (b) comprises an indication of the secondpriority score for the second claim and respective priority scores forthe second plurality of claims; prioritize the first claim and the firstplurality of claims in the first queue data structure, and (b) thesecond claim and the second plurality of claims in the second queue datastructure; and dynamically provide an update to (a) a first userinterface element associated with the first queue data structure, and(b) a second user interface element associated with the second queuedata structure, wherein the user interface displays the update (a) tothe first user interface element, and (b) the second user interfaceelement.
 16. The computing system of claim 15, wherein the computingsystem is further configured to: extract a first feature set from a setof training claims; determine a claim-related rate for each claim of theset of training claims, wherein the claim-related rate is a targetoutput of the one or more machine learning models; and store the firstfeature set and the claim-related rates for the set of training claimsas a training dataset.
 17. The computing system of claim 16, wherein thecomputing system is further configured to automatically detect a changein the first feature set and the first claim.
 18. The computing systemof claim 16, wherein the computing system is further configured to trainthe one or more machine learning models based at least in part on thetraining dataset.
 19. The computing system of claim 15, wherein thepriority score for the first claim is the product of the predicted claimscore for the first claim and a first recoverable amount for the firstclaim.
 20. The computing system of claim 15, wherein dynamicallyproviding the update to the first user interface element and the seconduser interface element comprises pushing the update.
 21. The computingsystem of claim 15, wherein the computing system is further configuredto: determine a predicted claim score for each of the first plurality ofclaims; and determine a priority score for each of the first pluralityof claims.